Release of 2nd Edition of the Elements of Statistical Learning
January 2, 2014
The release of the 2nd edition of The Elements of Statistical Learning is now available through the Stanford Statistics Department. The book was created in response to the massive leaps in computer and information technology in the last ten years by authors Trevor Hastie, Robert Tibshirani and Jerome Friedman. All are professors of statistics at Stanford, and the book does take a statistical approach but is concept-centered rather than focusing on mathematics.
The article summarizes the content:
“Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting–the first comprehensive treatment of this topic in any book.”
Sounds like another goody for the artificial intelligence fan. The book is aimed at data analysts or theory junkies and is absent of code. In a review, D.J. Hand calls it “a beautiful book” in both presentation and content. His only criticism that if the book were to be used for an undergrad or grad level course it should be supplemented with more practical approach utilizing S-PLUS or R language, if that can be called a criticism when paired with his praise of the authors and their work.
Chelsea Kerwin, January 02, 2014
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